import pandas as pd

wide = pd.DataFrame(
    {
        "region": ["North", "South"],
        "product": ["Widget", "Widget"],
        "q1_sales": [120, 90],
        "q2_sales": [135, 104],
        "q3_sales": [148, 110],
    }
)

id_columns = ["region", "product"]
value_columns = ["q1_sales", "q2_sales", "q3_sales"]

long = wide.melt(
    id_vars=id_columns,
    value_vars=value_columns,
    var_name="quarter",
    value_name="sales_usd",
)

expected_rows = len(wide) * len(value_columns)

print(f"pandas {pd.__version__}")
print()
print("WIDE_DATA")
print(wide.to_string(index=False))
print()
print("LONG_DATA")
print(long.to_string(index=False))
print()
print("VERIFY_SHAPE")
print(f"wide rows: {len(wide)}")
print(f"value columns: {len(value_columns)}")
print(f"expected long rows: {expected_rows}")
print(f"actual long rows: {len(long)}")
print(f"columns: {', '.join(long.columns)}")
print()
print("SALES_BY_QUARTER")
print(long.groupby("quarter", sort=False)["sales_usd"].sum().to_string())